Another line of work has focused on non-asymptotic results in an Euclidean setting with features that have a covariance matrix with decaying eigenvalues, see [1]} for classification with support-vector machines (SVM) and [2]}, [3]} for linear regression. These results rely crucially on Euclidean geometry, which gives explicit formulas for the estimators under consideration, and also do not lead to improved convergence rates in the presence of low-dimensional intrinsic structure.
